Leveraging Enriched Skeleton Representation With Multi-Relational Metrics for Few-Shot Action Recognition

Few-shot action recognition aims to identify new action classes with limited training samples. Most existing methods overlook the low information content and diversity of skeleton features, failing to exploit useful information in rare samples during meta-training. This leads to poor feature discriminability and recognition accuracy. To address both issues, we propose a novel Enriched Skeleton Representation and Multi-relational Metrics (ESR-MM) method for skeleton-based few-shot action recognition. First, a Frobenius Norm Diversity Loss is introduced to enrich skeleton representation by maximizing the Frobenius norm of the skeleton feature matrix. This mitigates over-smoothing and boosts information content and diversity. Leveraging these enriched features, we propose a multi-relational metrics strategy exploiting cross-sample task-specific information, intra-sample temporal order, and inter-sample distance. Specifically, Support-Adaptive Attention leverages task-specific cues between samples to generate attention-enhanced features. Then, the Bidirectional Temporal Coherent Mean Hausdorff Metric integrates Temporal Coherence Measure into the Bidirectional Mean Hausdorff Metric for class separation by accounting for temporal order. Finally, Prototype-discriminative Contrastive Loss exploits distances from class prototypes to query samples. ESR-MM demonstrates superior performance on two benchmarks.